Comorbidities among drug, alcoholism, and mental health disorders are significant factors in the initiation, intensity, types, and costs of treatment. Numerous studies have shown the incidence of multiple morbidities to be related to increased drug abuse treatment costs. Yet, finds have generally been limited to particular clinical populations, with limited information on treatment for non-ADM conditions. The proposed project seeks to determine: (1) likelihood and timing of the initiation of drug abuse treatment within a 3 year window; (2) impacts of alcoholism and mental health comorbidities on the likelihood and timing of the drug abuse treatment; (3) once treatment begins, the duration of the episode; (4) impact to time to treatment initiation, and episode length, on episode costs; (5) relation of insurance coverage to the timing and extent of the ADM treatments; (6) policy implications of parts (1) - (5) in the context of health resources planning. We seek to investigate the predictors of initiation and timing of treatment, and the lengths of episodes of both outpatient and inpatient care for persons treated for drug, alcoholism, and mental health disorders. The database contains almost 34,000 individuals who used either drug abuse or alcoholism treatment in a three year period from 1989 through 1991. The database permits analysis of drug treatments and recognizes the interrelated nature of the ADM (alcoholism, drug, and mental disorder) treatments. Health services researchers have recently adopted ~hazard~ models to explain the initiation, timing, and duration of treatments. Hazard models originally focused on ~time to failure~ (whence hazard ) of light bulbs; biomedical researchers have used hazard models to examine the length of survival after the diagnosis of a disease, or after a major operation. The project team has recently applied hazard analysis to the length of alcoholism treatment episodes with excellent results. The models will help to predict: (1) When patients present themselves for drug abuse treatment; and if they present, (2) how long the treatment spells last. These predictions will provide valuable information on utilization rates, patterns of care, and treatment episode costs.